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- Title
Predictive Modeling in Race Walking.
- Authors
Wiktorowicz, Krzysztof; Przednowek, Krzysztof; Lassota, Lesław; Krzeszowski, Tomasz
- Abstract
This paper presents the use of linear and nonlinear multivariable models as tools to support training process of race walkers. These models are calculated using data collected from race walkers’ training events and they are used to predict the result over a 3 km race based on training loads. The material consists of 122 training plans for 21 athletes. In order to choose the best model leave-one-out cross-validation method is used. The main contribution of the paper is to propose the nonlinear modifications for linear models in order to achieve smaller prediction error. It is shown that the best model is a modified LASSO regression with quadratic terms in the nonlinear part. This model has the smallest prediction error and simplified structure by eliminating some of the predictors.
- Subjects
WALKING (Sports); MULTIVARIABLE calculus; SPORT walking training; ATHLETES; LINEAR statistical models
- Publication
Computational Intelligence & Neuroscience, 2015, Vol 2015, p1
- ISSN
1687-5265
- Publication type
Article
- DOI
10.1155/2015/735060